James Chapman

Materials Chemistry · with Batra · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom James Chapman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. J. Behler
  2. Matthias Rupp 
  3. Roberto Car
  4. Ellad B. Tadmor

Papers from James Chapman's perspective

Ordered by how strongly the researchers James Chapman trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
  2. The elements of statistical learning: data mining, inference, and prediction, 2nd Edition
  3. Unified approach for molecular dynamics and density-functional theory.
  4. Adaptive machine learning framework to accelerate ab initio molecular dynamics
  5. Machine learning for quantum mechanics in a nutshell
  6. Modeling Materials: Continuum, Atomistic and Multiscale Techniques
  7. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations.
  8. Atom-centered symmetry functions for constructing high-dimensional neural network potentials.
  9. The Elements of Statistical Learning
  10. Generalized neural-network representation of high-dimensional potential-energy surfaces.

Their papers